Semiparametric regression summary

Description

Takes a fitted spm object produced by spm() and summarises the fit.

Usage

## S3 method for class 'spm'
summary(object,...)

Arguments

Details

Produces tables for the linear (parametric) and non-linear (nonparametric) components. The linear table provides coefficient estimates, standard errors and p-values. The non-linear table provides degrees of freedom values and other information.

Value

The function generates summary tables.

Author(s)

M.P. Wand mwand@uow.edu.au (other contributors listed in SemiPar Users' Manual).

References

Ruppert, D., Wand, M.P. and Carroll, R.J. (2003)
Semiparametric Regression Cambridge University Press.
http://stat.tamu.edu/~carroll/semiregbook/

Ganguli, B. and Wand, M.P. (2005)
SemiPar 1.0 Users' Manual.
http://www.uow.edu.au/~mwand/SPmanu.pdf

See Also

spm plot.spm predict.spm

Examples

library(SemiPar)
data(onions)
attach(onions)
log.yield <- log(yield)
fit <- spm(log.yield~location+f(dens))
summary(fit)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(SemiPar)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/SemiPar/summary.spm.Rd_%03d_medium.png", width=480, height=480)
> ### Name: summary.spm
> ### Title: Semiparametric regression summary
> ### Aliases: summary.spm
> ### Keywords: models smooth regression
> 
> ### ** Examples
> 
> library(SemiPar)
> data(onions)
> attach(onions)
> log.yield <- log(yield)
> fit <- spm(log.yield~location+f(dens))
> summary(fit)


Summary for linear components:

             coef      se  ratio p-value
intercept  5.3880 0.24230  22.24       0
location  -0.3325 0.02388 -13.92       0




Summary for non-linear components:

           df  spar knots
f(dens) 4.213 63.02    17


> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>